Road Profile Sensor: A Detection Method for Active Suspension Systems

Abstract—Active suspension systems adjust the suspension components of an automobile to adapt to bumps or potholes that are encountered in the road as the vehicle is driving. These systems have the potential to improve safety, performance, and ride comfort in automobiles. An integral part of active suspension systems is a device to detect irregularities in the road. Current detection systems that are available lack either in precision, resolution, or speed. A senior design project, Dynamic Automatic Adjusting Suspension (DAAS), at Seattle Pacific University expressed a need for a high-performance road scanner that could be paired with their suspension system. The design would need to take into account problems with latency and resolution. I therefore began development of the Road Profile Sensor (RPS). The RPS was implemented through the design of a range finder that uses a linear photodiode array paired with a laser to measure distance. The distance from the sensor to the road changes as irregularities in the road are encountered, and this change in distance is measured by the RPS to determine the size of the irregularity. The proposed system runs on a soft processor core in an FPGA chip that is both a part of the DAAS system and communicates with the DAAS suspension controller. The RPS can be sampled 62 times per second and has height resolution of 4mm. With further development, the RPS has the potential to run at very high speeds in relatively low-power, low-cost FPGA’s. This design will yield much greater resolution in road scanning, which will lead to better suspension control, and a generally more reliable active suspension system. These improvements in road irregularity detection are expected to improve isolation between the road and the chassis of the vehicle, thereby improving the vehicle’s handling, versatility, and safety

Reviews may overestimate the effectiveness of medicines for back pain: Systematic review and meta-analysis

Objective: Systematic-reviews of analgesics for low back pain generally include published data only. Obtaining data from unpublished trials is potentially important because they may impact effect sizes in meta-analyses. We determined whether including unpublished data from trial registries changes the effect sizes in meta-analyses of analgesics for low back pain. Study Design and Setting: Trial registries were searched for unpublished data that conformed to the inclusion criteria of n=5 individual source systematic-reviews. We reproduced the meta-analyses using data available from the original reviews then re-ran the same analyses with the addition of new unpublished data. Results: Sixteen completed, unpublished, trials were eligible for inclusion in four of the source reviews. Data were available for five trials. We updated the analyses for two of the source reviews. The addition of data from two trials reduced the effect size of muscle relaxants, compared to sham, for recent-onset low back pain from -21.71 (95%CI -28.23 to -15.19) to -2.34 (95%CI -3.34 to -1.34) on a 0-100 scale for pain intensity. The addition of data from three trials (one enriched design) reduced the effect size of opioid analgesics, compared to sham, for chronic low back pain from -10.10 (95%CI -12.81 to -7.39) to -9.31 (95%CI -11.51 to -7.11). The effect reduced in the subgroup of enriched design studies, from -12.40 (95%CI -16.90 to -7.91) to 11.34 (95%CI -15.36 to -7.32), and in the subgroup of non-enriched design studies; from -7.27 (95%CI -9.97 to -4.57) to -7.19 (95%CI -9.24 to -5.14). Conclusion: Systematic-reviews should include reports of unpublished trials. The result for muscle relaxants conflicts with the conclusion of the published review and recent international guidelines. Adding unpublished data strengthens the evidence that opioid analgesics have small effects on persistent low back pain and more clearly suggests these effects may not be clinically meaningful

Systematic reviews that include only published data may overestimate the effectiveness of analgesic medicines for low back pain: A systematic review and meta-analysis

Objective: Systematic reviews of analgesics for low back pain generally include published data only. Obtaining data from unpublished trials is potentially important because they may impact effect sizes in meta-analyses. We determined whether including unpublished data from trial registries changes the effect sizes in meta-analyses of analgesics for low back pain. Study Design and Setting: Trial registries were searched for unpublished data that conformed to the inclusion criteria of n = 5 individual source systematic reviews. We reproduced the meta-analyses using data available from the original reviews and then reran the same analyses with the addition of new unpublished data. Results: Sixteen completed, unpublished, trials were eligible for inclusion in four of the source reviews. Data were available for five trials. We updated the analyses for two of the source reviews. The addition of data from two trials reduced the effect size of muscle relaxants, compared with sham, for recent-onset low back pain from −21.71 (95% CI: −28.23 to −15.19) to −2.34 (95% CI: −3.34 to −1.34) on a 0–100 scale for pain intensity. The addition of data from three trials (one enriched design) reduced the effect size of opioid analgesics, compared with sham, for chronic low back pain from −10.10 (95% CI: −12.81 to −7.39) to −9.31 (95% CI: −11.51 to −7.11). The effect reduced in the subgroup of enriched design studies, from −12.40 (95% CI: −16.90 to −7.91) to −11.34 (95% CI: −15.36 to −7.32), and in the subgroup of nonenriched design studies, from −7.27 (95% CI: −9.97 to −4.57) to −7.19 (95% CI: −9.24 to −5.14). Conclusion: Systematic reviews should include reports of unpublished trials. The result for muscle relaxants conflicts with the conclusion of the published review and recent international guidelines. Adding unpublished data strengthens the evidence that opioid analgesics have small effects on persistent low back pain and more clearly suggests these effects may not be clinically meaningful

RNA sequencing and lipidomics uncovers novel pathomechanisms in recessive X-linked ichthyosis

Recessive X-linked ichthyosis (RXLI), a genetic disorder caused by deletion or point mutations of the steroid sulfatase (STS) gene, is the second most common form of ichthyosis. It is a disorder of keratinocyte cholesterol sulfate retention and the mechanism of extracutaneous phenotypes such as corneal opacities and attention deficit hyperactivity disorder are poorly understood. To understand the pathomechanisms of RXLI, the transcriptome of differentiated primary keratinocytes with STS knockdown was sequenced. The results were validated in a stable knockdown model of STS, to confirm STS specificity, and in RXLI skin. The results show that there was significantly reduced expression of genes related to epidermal differentiation and lipid metabolism, including ceramide and sphingolipid synthesis. In addition, there was significant downregulation of aldehyde dehydrogenase family members and the oxytocin receptor which have been linked to corneal transparency and behavioural disorders respectively, both of which are extracutaneous phenotypes of RXLI. These data provide a greater understanding of the causative mechanisms of RXLI’s cutaneous phenotype, and show that the keratinocyte transcriptome and lipidomics can give novel insights into the phenotype of patients with RXLI

Development and measurement properties of the AxEL (attitude toward education and advice for low-back-pain) questionnaire

Introduction: Clinician time and resources may be underutilised if the treatment they offer does not match patient expectations and attitudes. We developed a questionnaire (AxEL-Q) to guide clinicians toward elements of first-line care that are pertinent to their patients with low back pain. Methods: We used guidance from the COSMIN consortium to develop the questionnaire and evaluated it in a sample of people with low back pain of any duration. Participants were recruited from the community, were over 18 years and fluent in English. Statements that represented first-line care were identified. Semantic scales were used to measure attitude towards these statements. These items were combined to develop the questionnaire draft. Construct validity was evaluated with exploratory factor analysis and hypotheses testing, comparing to the Back Beliefs Questionnaire and modified Pain Self-Efficacy Questionnaire. Reliability was evaluated and floor and ceiling effects calculated. Results: We recruited 345 participants, and had complete data for analysis for 313 participants. The questionnaire draft was reduced to a 3-Factor questionnaire through exploratory factor analysis. Factor 1 comprised 9 items and evaluated Attitude toward staying active, Factor 2 comprised 4 items and evaluated Attitude toward low back pain being rarely caused by a serious health problem, Factor 3 comprised 4 items and evaluated Attitude toward not needing to know the cause of back pain to manage it effectively. There was a strong inverse association between each factor and the Back Beliefs Questionnaire and a moderate positive association with the modified Pain Self-Efficacy Questionnaire. Each independent factor demonstrated acceptable internal consistency; Cronbach α Factor 1 = 0.92, Factor 2 = 0.91, Factor 3 = 0.90 and adequate interclass correlation coefficients; Factor 1 = 0.71, Factor 2 = 0.73, Factor 3 = 0.79. Conclusion: This study demonstrates acceptable construct validity and reliability of the AxEL-Q, providing clinicians with an insight into the likelihood of patients following first-line care at the outset

The RESOLVE Trial for people with chronic low back pain: Statistical analysis plan

Background: Statistical analysis plans describe the planned data management and analysis for clinical trials. This supports transparent reporting and interpretation of clinical trial results. This paper reports the statistical analysis plan for the RESOLVE clinical trial. The RESOLVE trial assigned participants with chronic low back pain to graded sensory-motor precision training or sham-control. Results: We report the planned data management and analysis for the primary and secondary outcomes. The primary outcome is pain intensity at 18-weeks post randomization. We will use mixed-effects models to analyze the primary and secondary outcomes by intention-to-treat. We will report adverse effects in full. We also describe analyses if there is non-adherence to the interventions, data management procedures, and our planned reporting of results. Conclusion: This statistical analysis plan will minimize the potential for bias in the analysis and reporting of results from the RESOLVE trial. Trial registration: ACTRN12615000610538 (https://www.anzctr.org.au/Trial/Registration/ TrialReview.aspx?id=368619). © 2020 Associac¸ao˜ Brasileira de Pesquisa e Pos-Graduac ´ ¸ao˜ em Fisioterapia. Published by Elsevier Editora Ltda. All rights reserved

Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges

The role impairment associated with mental disorder risk profiles in the WHO World Mental Health International College Student Initiative

OBJECTIVE: The objective of this study is to assess the contribution of mental comorbidity to role impairment among college students. METHODS: Web-based self-report surveys from 14,348 first-year college students (Response Rate [RR] = 45.5%): 19 universities, eight countries of the World Mental Health International College Student Initiative. We assessed impairment (Sheehan Disability Scales and number of days out of role [DOR] in the past 30 days) and seven 12-month DSM-IV disorders. We defined six multivariate mental disorder classes using latent class analysis (LCA). We simulated population attributable risk proportions (PARPs) of impairment. RESULTS: Highest prevalence of role impairment was highest among the 1.9% of students in the LCA class with very high comorbidity and bipolar disorder (C1): 78.3% of them had severe role impairment (vs. 20.8%, total sample). Impairment was lower in two other comorbid classes (C2 and C3) and successively lower in the rest. A similar monotonic pattern was found for DOR. Both LCA classes and some mental disorders (major depression and panic, in particular) were significant predictors of role impairment. PARP analyses suggest that eliminating all mental disorders might reduce severe role impairment by 64.6% and DOR by 44.3%. CONCLUSIONS: Comorbid mental disorders account for a substantial part of role impairment in college students. © 2018 John Wiley & Sons, Ltd

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease